Interpretive Summary: Genomic selection (GS) is a method to predict the performance of new breeding lines by using markers throughout the genome. The method estimates parameters for a prediction model using a “training population” that has both marker and trait information. Predictions are then calculated on a separate validation population. We evaluated the accuracy of GS using data from five traits on 446 oat lines each genotyped with 1005 markers. Our objectives were to: 1) determine the effects of marker density and training population size on prediction accuracy; 2) evaluate accuracy when the training population is composed of older breeding lines; and 3) examine accuracy when the training and validation population are not closely related. Accuracy increased as the number of markers and training size become larger. Including older lines in the training population increased or maintained accuracy, indicating that older generations retained information useful for prediction. When training and validation subpopulations were closely related accuracy was greater than when they were distantly related. Across many scenarios involving large training populations, the accuracy of the two GS methods we tested did not differ. This empirical study provided guides for oat and other small grains breeders to improve their implementation of GS to hasten the delivery of improved cultivars.

Technical Abstract:
Genomic selection (GS) is a method to estimate the breeding values of individuals by using markers throughout the genome. We evaluated the accuracies of GS using data from five traits on 446 oat lines genotyped with 1005 Diversity Array Technology (DArT) markers and two GS methods (RR-BLUP and BayesC') under various training designs. Our objectives were to: 1) determine accuracy under increasing marker density and training population size; 2) assess accuracies when data is divided over time; and 3) examine accuracy in the presence of population structure. Accuracy increased as the number of markers and training size become larger. Including older lines in the training population increased or maintained accuracy, indicating that older generations retained information useful for predicting validation populations. The presence of population structure affected accuracy: when training and validation subpopulations were closely related accuracy was greater than when they were distantly related, implying that LD relationships changed across subpopulations. Across many scenarios involving large training populations, the accuracy of BayesC' and RR-BLUP did not differ. This empirical study provided evidence regarding the application of GS to hasten the delivery of cultivars through the use of inexpensive and abundant molecular markers available to the public sector.